The Duke AI Health Community of Practice is pleased to announce the Duke Machine Learning Summer School 2025: Generative AI (MLSS-GenAI), a 5-day, in-person class that provides lectures on the fundamentals of generative AI methods and applications. The curriculum is designed for individuals interested in learning about generative AI, with a focus on recent deep learning methodologies. MLSS-GenAI will introduce the mathematics and statistics at the foundation of current generative and representation learning models and provide hands-on training in the latest machine learning software using the PyTorch framework.

Strength in mathematics and statistics is a significant plus and will make all MLSS-GenAI material more accessible; however, it is not required to benefit from much of the program. Concepts will be introduced as intuitively as possible, with minimal math and technical details. As concepts are developed, more math will be introduced, but only the minimum needed to explain concepts. Case studies will demonstrate how the technology is used in practical generative AI applications. Finally, the class will introduce participants to coding software used to make such technology work in practice.

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Thank you to everyone who participated in Duke AI Health impact survey! We received a lot of great feedback, and we look forward to sharing the results in next month’s newsletter. As a quick preview, over 95% of survey participants who had interacted at least once with AI Health in the last year reported that those interactions changed how they thought about AI; October’s Innovation Summit and the Friday News Roundup were participant favorites.

The Patient-Centered Outcomes Research Institute (PCORI) has awarded Duke University Health System funding to implement electronic monitoring of patients’ self-reported symptoms during cancer treatment. This project integrates Duke’s strengths in digital health and implementation science to make symptom monitoring a routine part of cancer care. Co-led by AI Health Senior Advisory Board Member Dr. Richard Shannon, who also serves as chief medical officer and chief quality officer for Duke Health, and Duke hematologist/oncologist Dr. Thomas LeBlanc, it builds on patient-centered research to improve well-being, reduce hospital visits, and support treatment adherence. Original story at link below by Liz Switzer (Duke Medicine)

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Research in stroke risk prediction is enhanced by the inclusion of a broad range of data from different patient cohorts that strengthen the evidence for the scientific questions being investigated. In two recent AI Health Virtual Seminars, researchers from Duke AI Health and the American Heart Association (AHA) introduced an open metadata repository designed to give researchers access to the processes and methods used to harmonize data from four large, National Institutes of Health-funded cohort studies.

In the first seminar, presenters explained how the datasets were harmonized and demonstrated how users can access and utilize the metadata repository. In a follow-up seminar, leading researchers presented new methodologies and results from studies that were conducted with the harmonized dataset. The repository offers resources and tools for studies in stroke risk prediction, and researchers are invited to review the seminar recordings to learn more about how they can leverage it for their own work.

Watch the recordings to learn how the metadata repository supports research into stroke risk prediction:

• Part 1: Facilitating Harmonization of Variables from the Framingham, MESA, ARIC, and REGARDS Studies Through a Metadata Repository
• Part 2: Research Applications with Harmonized Variables from the Framingham, MESA, ARIC, and REGARDS Studies

The recent 2025 Responsible AI Symposium, co-sponsored by The Society-Centered AI Initiative at Duke, the Duke Artificial Intelligence Master of Engineering, Duke AI Health, the Duke Office of Climate and Sustainability, and the Coach K Center for Leadership and Ethics (COLE), took place this past February and March at Duke University’s Karsh Alumni Center. The four-day event, which included industry and academic keynote talks, research talks, a poster session and a hackathon, convened a wide array of leaders, researchers, and students whose work is focused on society-centered AI and responsible AI.

Programming, speaker bios, event photos, and video from the 2025 Symposium are all available at the link below, where information about the planned 2026 event will posted when available. Abstracts from research talks are also available at the site.

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In a recent session of the weekly NIH Pragmatic Trials Collaboratory Grand Rounds series (recording and slides available at link below), Duke AI Health Faculty Affiliate Matthew Engelhard, MD, PhD, (left) and Anivarya Kumar, (right) a fourth-year medical student at Duke University School of Medicine, presented a talk titled “A Cross-Sectional Study of GPT-4–Based Plain Language Translation of Clinical Notes to Improve Patient Comprehension of Disease Course and Management.” In this presentation, Engelhard and Kumar described a project that employed the widely available GPT-4 large-language model to render physicians’ clinical notes into plain-language versions that would be more readily understood by patients.

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Duke Orthopaedics Department Research Day was celebrated on March 18 with standing-room-only attendance. This year’s theme, “BIG3: Big Ideas, Big Data, and Big Analysis,” sparked insightful conversations about the transformative potential of big data and artificial intelligence in addressing today’s most pressing challenges. Featured speakers included Michael Pencina, PhD, Chief Data Scientist for Duke Health, Vice Dean for Data Science, and Director of Duke AI Health; Elizabeth R. Skidmore, PhD, OTR/L, FAOTA, FACRM, from the University of Pittsburgh; and Duke University School of Medicine faculty Michael Bolognesi, MD; Maggie Horn, DPT, MPH, PhD; and Christian Pean, MD, MS. The event recording, photo gallery, full program booklet, and award recipients are available online.

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Duke’s Protected Analytics Computing Environment (PACE) is a secure platform for research teams working with sensitive health data. This year, thanks to the support of the School of Medicine, PACE will undergo a major upgrade to enhance computational power, security, and ease of use. PACE 2.0 will introduce a high-performance computing environment, GPU capabilities for AI research, and other new tools made accessible through Open OnDemand. Upgrades will roll out in late spring through the summer of 2025. Current PACE users at Duke University interested in beta testing PACE 2.0 are invited to sign up using the link below (log-in required).

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A recent article* featured on Duke’s “Made for This” website highlights work by Duke computer science professor Nicki Washington and Duke AI Health Director Michael Pencina aimed at developing and promoting ethical approaches to building and implementing artificial intelligence.

“Both Pencina and Washington are committed to the idea that progress should not come at the expense of equity. At Duke, their work aligns to ensure that technological advancement, whether in health care or computer science, is both ethical and inclusive.”

*Original article by Mary-Russell Roberson, Leslie Gray Baker & Megan Hujber

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Duke Datathon 2025

Symposium: Fri, April 25, 2025 | Datathon: Sat-Sun, April 26-27, 2025

In person at the Duke Health Center for Interprofessional Education and Care (IPEC) Building

Link to registration here

 

Overview

Impact critical care in new ways at the upcoming Datathon! From Duke Critical Care Informatics, the Datathon is a collaborative two-day event that connects critical care clinicians with data scientists to develop pragmatic data-driven models using de-identified critical care electronic health record datasets. The theme is “Data Science in Critical/Acute Care.”

Using de-identified critical care electronic health record datasets (e.g., MIMIC, eICU), we will develop new projects in 36 hours, from problem to abstract (and more)!

Participants will be organized into teams that are half-data science, half-clinical. You do not need to have a team; we will help you find a team. Questions will be crowdsourced. No experience is required.

• If you’re a clinician, your interest, but not expertise, in data science is required.
• If you’re a data scientist, your interest, but not expertise, in healthcare and critical care is required.

Registration open to academic institutions in Dec 2024.  Registration rates will increase in Feb 2025.

 

Registration

Early registration fees (until February 28) are structured as follows:

• Data Science: Faculty & Community/Industry – $200.00
• Clinical: Faculty & Community/Industry – $200.00
• Data Science: Staff – $150.00
• Clinical: Staff – $150.00
• Data Science: Trainee – $50.00
• Clinical: Trainee – $50.00

Please register early! Registration fees will increase starting on March 1.

 

Sponsorship

We thank the following sponsors for their support: Duke AI Health; CHoRUS (Clinical Care AI through the CHoRUS Network); Society of Critical Care Medicine; Duke Department of Biostatistics & Bioinformatics; Duke Master in Interdisciplinary Data Science (MIDS); Duke University Division of Anaesthesia / Critical Care.

 

Opportunities for sponsorship are still available and are structured as follows:

• Bronze-level sponsorship ($5k): position for tabletop display and signage and recognition as a snack sponsor.
• Silver-level sponsorship ($10k): position for tabletop display and signage, recognition as a coffee-break sponsor and 3 reserved registrations.
• Gold-level sponsorship ($20k): preferred position for tabletop display and signage, recognition as a meal break sponsor and 5 reserved registrations.
• Platinum-level sponsorship ($30k): prime position for tabletop display and signage, recognition as a meal sponsor, invitation to reception with opportunity to address attendees and 10 reserved registrations.
• All sponsors receive acknowledgement in datathon marketing materials
• All sponsors receive verbal recognition each day by meeting host

For sponsorship opportunities, please contact Ian Wong, MD, PhD (a.ian.wong@duke.edu)

 

Event directors:

Ian Wong, MD, PhD; Assistant Professor of Medicine; Assistant Professor in Biostatistics & Bioinformatics

Ricardo Henao, PhD; Associate Professor of Biostatistics & Bioinformatics

 

Read more at https://sites.duke.edu/datathon2025/

(including Colab Notebooks, resources from previous datathons, and other information)

Earlier this past month, Duke AI Health and Duke University’s Pratt School of Engineering hosted a three-day gathering that brought together clinical insight, engineering expertise, and industry perspectives on health AI. The overall goal of The Duke Summit on AI Health for Innovation, which took place on October 9-11 at the J.B. Duke Hotel on Duke University campus, was to “foster a community of practice around health-oriented AI development that bridges the medical and engineering fields.” Featuring keynote addresses from Jerome P. Lynch, PhD, Vinik Dean of Duke’s Pratt School of Engineering, and Duke Health Chief Quality Officer Richard Shannon, MD, the summit also featured numerous panel discussions, a “fireside chat” hosted by Duke AI Health Director Michael Pencina, PhD, and interactive sessions centered on “design thinking” principles.

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Duke AI Health welcomes Michael Pignone, MD, MPH, as he takes the helm of the Duke Collaborative to Advance Health Equity (CACHE) effective October 15, 2024. Currently the Rebecca and John Kirkland Distinguished Professor of Medicine and vice chair of Quality and Innovation in the Duke Department of Medicine, Dr. Pignone also serves as the faculty director for Primary Care Transformation and Innovation within the Margolis Center for Health Policy and the Director for Cancer Screening Equity at the Duke Cancer Institute.

“I am delighted to see Dr. Pignone move into this role,” says Richard Shannon, MD, founding director of CACHE and Chief Quality Officer & Chief Medical Officer for Duke Health. “The energy he brings to this work and the breadth of his experience, spanning clinical care, research, policy, and population health, make him an ideal choice to build upon our early successes with this initiative and grow the community partnerships vital to our work.”

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Duke University and Duke-NUS Medical School in Singapore’s AI + Medical Sciences Initiative (DAISI) invite you to a virtual symposium on AI, in partnership with the Duke AI Health Community of Practice.

This two-hour event on Thursday, June 6 from 8am – 10am (EDT) will feature lightning talks — short, fast-paced presentations — presented by faculty and staff involved in health-related topics.

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By Jessica Johnstone

A successful poster session held at the Duke’s School of Nursing this past March marked the completion of a new writing workshop series conducted by the Duke AI Health Community of Practice. This initiative, titled “Foundations of Scientific Writing for Staff Members,” was focused on helping operational staff develop and extend their scientific and scholarly communication skills.

“Although we made use of a traditional lecture-style format, we also designed the series with an eye to interactivity and student participation,” said Jonathan McCall, MS, communications director for Duke AI Health.

McCall, who helped lead the didactic portions of the workshops, went on to note that the organizers didn’t want the virtual classes, held via Zoom, to consist solely of “talking head” instruction.

“One feature that arose almost through improvisation – using small breakout sessions during the classes – helped keep things engaging for our learners,” he added.

The workshop series invited individuals with varying degrees of experience in scientific writing but was particularly focused on encouraging participation from a broad spectrum of staff, including coordinators, project managers, project leaders, and analysts. Learners had the opportunity to choose poster topics that aligned with their interests and experience in operational processes, program descriptions, communication strategies, and more.

“I’m thrilled to witness the growth and enthusiasm among participants as they honed their scientific writing skills throughout the workshop series,” said project manager Jessica Johnstone, who helped organize and run the workshop series while also participating in it as learner.

The workshop series, which was produced in collaboration with the Duke Clinical and Translational Science Institute (CTSI), comprised five sequential sessions that addressed basic principles of scholarly writing and publication and provided a foundation for hands-on experience developing posters. Although classes were conducted remotely, the culminating poster session, held on March 13^th at the Christine Siegler Pearson Building at the School of Nursing, was an in-person event. Incorporated into the first day of the Fostering AI/ML Research for Health Equity and Learning Transformation (FAIR HEALTH™) symposium hosted by DUSON professor Michael Cary, PhD, RN, the interactive poster session gave participants a forum to showcase their work and gain experience both in presenting and discussing it.

“Our goal for this workshop series was to create a valuable addition to our employees’ training and development,” said workshop organizer Shelley Rusincovitch, MMCi, FAMIA, managing director of Duke AI Health and co-director of the CTSI Biomedical Informatics & Data Science (BIDS) Pillar. “We’re very glad to see this program succeed and we appreciate the support of CTSI’s leadership in making it possible.”

 

 

Input from ChatGPT (chat.openai.com) was used to revise and edit this article.

We are thrilled to invite you to our upcoming symposium, Fostering AI/ML Research for Health Equity and Learning Transformation (FAIR HEALTH™), scheduled for March 13-14, 2024, at the Duke University School of Nursing in Durham, NC. This two-day event is dedicated to advancing discussions on cutting-edge research and practices aimed at promoting equity and fairness in algorithmic systems.

Attendance is free and open to everyone. Please join us! Register at https://duke.qualtrics.com/jfe/form/SV_6RIrLwyKEyM8FOm

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Monday, January 8, 2024: Duke University recently hosted the Fostering AI/ML Research for Health Equity and Learning Transformation (FAIR HEALTH) Workshop at the Kirby Horton Hall in the scenic Sarah P. Duke Gardens in Durham, NC. This inclusive event was designed for individuals passionate about advancing healthcare through innovation while emphasizing equity and fairness in clinical algorithms. Attendees, including faculty, staff, and students, comprised a diverse audience committed to shaping the future of healthcare technology.

The FAIR HEALTH Workshop focused on the pressing issue of algorithmic bias in clinical decision-making, offering participants valuable insights and strategies to identify, evaluate, and mitigate biases in healthcare algorithms. With AI and machine learning playing increasingly pivotal roles, the imperative was to ensure these technologies don’t inadvertently perpetuate biases, promoting unequal treatment.

A distinguished panel of experts, featuring Duke AI Health’s Michael Cary, PhD, RN, Sophia Bessias, MPH, MSA, and Ben Goldstein, PhD, and Duke-Margolis Center for Health Policy’s Christina Silcox, PhD, led discussions into the legal and ethical dimensions surrounding the implementation of clinical algorithms. The panelists offered practical strategies applicable across the entire development lifecycle. Their aim extended beyond raising awareness about the challenges posed by algorithmic bias; they sought to equip attendees with the tools needed to effectively address these challenges.

The workshop’s contributions were pivotal, illuminating a path for advancing healthcare equity through innovative AI and machine learning solutions that enhance patient care while minimizing bias and prioritizing equity.

Another distinctive element of the FAIR HEALTH Workshop was its interactive nature. Organizers seamlessly integrated a combination of lectures and an engaging case study discussion to foster active participation. The case study honed in on a clinical prediction algorithm deployed at Duke Health, allowing participants to apply their bias-probing skills in a real-world scenario.

The panelists underscored the importance of considering legal and ethical implications in the development and deployment of clinical algorithms. The complex regulations surrounding AI in healthcare were demystified, providing clear guidance to participants. This knowledge is pivotal in navigating the evolving landscape of healthcare technology, ensuring that ethical considerations stand at the forefront of AI integration.

Attendee feedback underscored the success of the workshop, receiving a stellar rating of over 4.5 out of 5. The interactive discussions and practical examples emerged as key components that significantly enhanced the understanding of how biases can infiltrate clinical AI tools. Participants expressed gratitude for the opportunity to hone their skills in assessing data reliability and reducing bias, crucial elements for preventing AI systems from inadvertently favoring certain patient groups.

The FAIR HEALTH Workshop at Duke University marked a substantial stride in ongoing efforts to ensure fairness in AI used in patient care. The event successfully brought together a diverse group of professionals, fostering collaboration across disciplines, including clinicians, clinician scientists, social scientists, and technical experts. As AI integration in healthcare continues to burgeon, the commitment to its ethical application extends beyond professional boundaries, becoming a community responsibility.

By Jessica Johnstone

Friday, December 8, 2023 was a lively day as the Sarah P. Duke Gardens, Kirby Horton Hall was transformed into a hub of quantitative activity, hosting the Health Data Science Poster Showcase. It included 55 posters, each featuring insightful research, showcasing the depth and talent of Duke’s diverse community. From statistics and informatics to machine learning and data engineering, the topics spanned the full spectrum of health data science. Students, fellows, staff, and faculty alike presented their work, illuminating the collaborative spirit that thrives at Duke. The event, championed by Duke AI Health, the Duke Clinical & Translational Science Institute, the Laboratory for Transformative Administration, and the Center for Computational Thinking, was a testament to Duke’s commitment to fostering innovative research.

The event was a resounding success, fostering connections, sparking ideas, and leaving everyone energized by the potential of data-driven health. “The showcase illuminated the depth and impact of health data science across Duke,” Shelley Rusincovitch, MMCi, the event’s director said. “Empowering these collaborations is precisely why we do what we do.”

Michael Pencina, PhD, Chief Data Scientist for Duke Health and Director of Duke AI Health, extended a warm welcome to the audience. He remarked, “This endeavor reflects Duke’s dedication to ethical equitable data science, showcasing the application of advanced natural language processing methods to extract meaningful insights for the future of healthcare.”

Akshay Bareja, PhD, an Assistant Professor in Duke Molecular Physiology Institute, presented the Best Computational Thinking awards from Duke’s Center for Computational Thinking, to Tanner J. Zachem, a PhD student in the Department of Neurosurgery at the Brain Tool Laboratory under Patrick Codd, and Aditya Parekh, MS, a Duke AI Health Data Science fellow in Rohit Singh’s lab. Dr. Bareja commented, “There were so many excellent posters presented. I was particularly struck by the novelty and sophistication of the winner (TumorID) and runner-up (Sceodesic). The Tumor ID team developed a florescence spectroscopy device that can accurately classify tumor type and shows promise as a tool that can be used during surgery to perform real-time tumor identification and classification. Inspired by how flight paths are computed, the Sceodesic team developed a novel gene-program discovery algorithm. The clear and immediate real-world impact of TumorID gave it the edge and the fact that the presenter, Tanner Zachem, is still a first-year PhD student made the achievement all the more impressive!”

Mr. Zavhem’s poster, “Machine Learning and Laser Induced Fluorescence Spectroscopy for In Vivo Brain Tumor Identification” included co-authors Rory Goodwin MD, PhD, and Patrick J. Codd MD. Mr. Parekh’s poster “Sceodesic: Navigating the Manifold of Single-cell Gene Coexpression to Discover Interpretable Gene Programs” included co-authors Sinan Ozbay, MFin, and Rohit Singh, PhD.

Duke Health Data Science Poster Showcase
Happening tomorrow: Friday, December 8, 2023 |  11:00 AM – 1:00 PM (Eastern time)
In person at the Sarah Duke Gardens, Kirby Horton Hall

The fall 2023 Health Data Science Poster Showcase will highlight the transformative work in health data science happening all across Duke. We’re excited that the showcase will feature 54 posters – our largest-ever showcase – with participation by departments, centers, and institutes from all across Duke.

We will serve a light lunch from 11:00-1:00, and we invite you to join the group photo at 12:00 PM and the poster award presentations at 12:30 PM. You can drop in for a few minutes, or stay for the entire time!

All are welcome, and we invite you to join us! Please do feel free to share and forward to others.

The event will be held in the Sarah Duke Gardens, in the visitor center Kirby Horton Hall (420 Anderson Street, Durham, NC 27708). This is just a short walk from the main hospital and clinics, and close to the C1 and H1 Duke Transit Routes. Free, reserved parking for this event is next to the building (through the Anderson Street entrance).

A groundbreaking paper co-authored by Duke Health’s Nicoleta Economou, PhD, and Michael Pencina, PhD, recently published in NPJ Digital Medicine discusses leveraging a Quality Management System (QMS) for AI/ML development intended for healthcare. The authors explain how a tailored QMS framework can bridge the AI translation gap, ensuring safe, ethical, and effective incorporation into patient care. This approach can accelerate the translation of AI research into practical clinical applications, prioritizing patient safety and fostering trust in healthcare innovation.

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FAIR HEALTH (Fostering AI/ML Research for Health Equity and Learning Transformation)
Monday, January 8, 2024, 9:00 AM – 12:00 PM
In person at Sarah Duke Gardens in Kirby Horton Hall

This event is open to anyone who is passionate about advancing healthcare through innovation while ensuring health equity and fairness in clinical algorithms, including faculty, staff, and students.

This workshop will delve into the critical issue of algorithmic bias and clinical decision making. This is an opportunity to gain essential insights and practical strategies to identify, mitigate, and evaluate bias in clinical algorithms. We will also explore the legal and ethical implications of algorithmic bias in healthcare, an aspect that is gaining increasing importance in today’s dynamic healthcare landscape. To enrich the workshop and encourage active participation, we have incorporated a combination of lectures and an interactive case study discussion, making it an even more rewarding experience for all attendees. By engaging in this workshop, and learning from one another, we can pave the way for a future where healthcare algorithms enhance patient care, minimize bias, and prioritize equity.

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Vanderbilt University Medical Center (VUMC) and Duke University School of Medicine were awarded a $1.25 million grant from the Gordon and Betty Moore Foundation for the project “Measuring Artificial Intelligence (AI) Maturity in Healthcare Organizations.”  Working with the Coalition for Health AI (CHAI) and the University of Iowa, a team of experts will leverage the grant to develop a maturity model framework. The project leads are Peter Embí, MD, MS, and Laurie Novak, PhD, MHSA, from VUMC; and Michael Pencina, PhD, and Nicoleta Economou, PhD, from Duke. This framework will outline the essential capabilities that health systems must establish to ensure they are well-prepared for the trustworthy utilization of AI models.

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The Duke Department of Surgery is pleased to announce the appointment of Ozanan R. Meireles, MD, as the department’s first Vice Chair for Innovation, effective Jan. 2, 2024.

Dr. Meireles is an internationally known authority in surgical applications of Artificial Intelligence (AI) and comes to us from Massachusetts General Hospital (MGH) where he specializes in minimally invasive surgery and runs the MGH Surgical AI and Innovation Lab (SAIIL). This lab boasts an impressive and longstanding close collaboration of more than eight years with MIT’s renowned Computer Science and Artificial Intelligence Lab (MIT-CSAIL), a partnership cultivated under the guidance of Professor Daniela Rus. Subsequently, he is bringing SAIIL to Duke as director of the lab. He will work within the School of Medicine as Surgical Director of Duke AI Health and advise on surgical AI applications emerging in the Health System. He will also serve as Vice Chair of Innovation for the Department of Surgery.

“We are delighted to welcome Dr. Meireles to Duke, where the intersection of medicine and data science serves as the cornerstone for innovation in health research and healthcare delivery,” says Michael Pencina, PhD, Chief Data Scientist for Duke Health, and Director of Duke AI Health. “His expertise in AI will play a vital role in advancing our data-driven collaborative initiatives, and we look forward to the groundbreaking discoveries and advancements that will result from his contributions.”

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Duke AI Health is pleased to announce the Duke Electronic Health Records Study Design Workshop (EHR-SDW) 2023. The workshop will be offered in December as a virtual five-day class (December 4 – 8, 2023) that provides foundational lectures and hands-on studios on the fundamentals of working with and designing EHR based studies.

The EHR-SDW is targeted toward individuals interested in learning about how to work with and conduct studies using electronic health records (EHR) data. EHR data are a widely available form of real-world data that have become standard in studies ranging from clinical trials, comparative effectiveness, risk prediction, and population health. The EHR-SDW will introduce the components of EHR data and introduce considerations for design of effective studies. In addition to didactic lectures, participants will get hands-on experience in working with publicly available tools to facilitate EHR studies (e.g., RxNorm, CCS codes, geocoding) as well as feedback on effective study designs that they will work on. The course will be conducted virtually via Zoom.

• To register for the EHR-SDW, please visit https://events.duke.edu/ehr-sdw-2023
• To request consideration for a scholarship, please visit https://duke.qualtrics.com/jfe/form/SV_a3Fs9TyyK82Bkr4

The deadline for registration is Tuesday, November 28, 2023.

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The next Duke Health Data Science poster showcase will be held on Friday, December 8, 2023 from 11:00 AM – 1:00 PM in person at the Sarah Duke Gardens Kirby Horton Hall.

We invite any member of the Duke community to submit a poster topic and participate in this event, including students, trainees, staff, and faculty.

Poster topics can cover a wide range of potential topics, such as statistics, informatics, machine learning, data engineering, implementation, process engineering, technology development, or applications. We especially encourage submissions describing experiences with Duke data sources. Posters describing class projects are also encouraged.

The preferred deadline for poster topics to be submitted is Wednesday, November 15, 2023.

Read more about this call for participation at https://aihealth.duke.edu/2023-cfp-poster-showcase/

Duke AI Health is pleased to announce the Duke Electronic Health Records Study Design Workshop (EHR-SDW) 2023. The workshop will be offered December 4^th through 8^th as a virtual five-day class that provides foundational lectures and hands-on studios on the fundamentals of working with and designing EHR based studies. The EHR-SDW is targeted toward individuals interested in learning about how to work with and conduct studies using electronic health records (EHR) data. EHR data are a widely available form of real-world data that have become standard in studies ranging from clinical trials, comparative effectiveness, risk prediction, and population health. The EHR-SDW will introduce the components of EHR data and introduce considerations for design of effective studies. In addition to didactic lectures, participants will get hands-on experience in working with publicly available tools to facilitate EHR studies (e.g., RxNorm, CCS codes, geocoding) as well as feedback on effective study designs that they will work on. The course will be conducted virtually via Zoom. This workshop is offered through Duke AI Health’s Health Data Science (HDS) program and builds on the success of the Electronic Health Records Study Design Workshop held in December 2022 and highly successful Machine Learning Schools, with 12 events held since 2017. The Duke Machine Learning Schools have reached hundreds of participants from academia and industry and including international audiences at the SingHealth/Duke NUS Medical School and the Duke Kunshan University campus. Our 2022 Duke Machine Learning Summer School attracted 140 participants from around the world, representing 41 universities, institutes, and corporations.

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Nicoleta Economou-Zavlanos, PhD, the director of Governance and Evaluation of health AI systems at Duke AI Health, was recently interviewed by AIMed’s Gemma Lovegrove for their AI Champions Interview Series, which highlights key thought leaders in the AI space. During the interview, Dr. Economou underscored the importance of incorporating fairness, transparency, and inclusivity throughout the entire process of health AI development, implementation, and monitoring.

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The Connected Health Initiative (CHI) is hosting an in-person conference titled ‘Artificial Intelligence and the Future of Digital Healthcare at the Crossroads’ on September 26, 2023, at the National Press Club in Washington, D.C., from 12:30 PM to 5:35 PM EDT. The event will delve into the profound impact of AI systems on healthcare, offering potential for improved outcomes, cost savings, and a shift towards proactive disease prevention. Duke AI Health Director Michael Pencina, PhD, ABCDS Director Nicoleta Economou-Zavlanos, PhD, and AI Health Equity Scholar Michael Cary, PhD, RN, will be presenting the Algorithm-Based Clinical Decision Support (ABCDS) Oversight framework at the conference, touching upon the program’s design, implementation and strategies for bias mitigation and ensuring health equity. The CHI conference aims to foster a vital public dialogue on the state of health AI, proactive approaches by leading organizations to address AI efficacy, and the government’s role in managing AI’s risks and opportunities in healthcare.

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Large language models (LLM) are powering amazing recent innovations in generative AI such as ChatGPT. Although their capabilities may seem like magic, behind these technologies are concepts that anyone can understand.

Please join us on Tuesday, September 19 for a lunch and learn as Larry Carin provides a math-free, intuitive explanation of how LLMs work. Dr. Carin will introduces participants to the deep-learning technology that has revolutionized the capacity of machines to perform language translation, to answer questions posed for given text, and to generate (synthesize) text that is near human-generated quality.

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Duke University and Duke Health have recently announced a monumental five-year strategic partnership with Microsoft to support artificial intelligence (AI) applications in medicine and lead transformation in healthcare delivery, champion health equity, and pioneer advanced research. “We are excited to partner with Microsoft and bring our organizations’ talent together to solve the most pressing healthcare challenges,” said Duke AI Health Director and Vice Dean for Data Science Michael Pencina, PhD. “We will combine medical expertise, data science methods, and technology solutions to improve patient care and community health and advance the foundations of trustworthy health AI.”

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Duke University Health System has received a new capacity-building contract with the Patient-Centered Outcomes Research Institute (PCORI). This contract, as part of PCORI’s Health Systems Implementation Initiative, will be used to support preparation for implementation projects that will advance the adoption of evidence-based practice within healthcare delivery settings. The Duke team, led by Rick Shannon, MD, includes co-investigators Armando Bedoya, MD; Nrupen Bhavsar, PhD; Ben Goldstein, PhD; and Michelle Lyn, MBA, MHA; and is supported by Duke AI Health.

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Duke AI Health and the Duke University School of Nursing are proud to announce the inaugural Duke Symposium on Algorithmic Equity and Fairness in Health, scheduled to take place in spring 2024.

The symposium will be spearheaded by Dr. Michael Cary, a distinguished scholar in nursing and the Elizabeth C. Clipp Term Chair of Nursing at the School of Nursing. Dr. Cary also serves as the Health Equity Scholar for Duke AI and leads the algorithmic equity initiative within Duke AI Health.

“In healthcare, algorithmic bias can lead to disparities in diagnosis, treatment recommendations, and access to care. It can disproportionately affect marginalized and underrepresented groups, exacerbating existing health inequities. As we rely increasingly on clinical algorithms to make decisions that impact people’s lives, we must continue to raise awareness about algorithmic bias in healthcare and work towards building a more equitable healthcare system,” stated Dr. Cary.

This groundbreaking symposium aims to bring together esteemed faculty members and experts from various disciplines to address bias resulting from clinical algorithms. The goal is to develop innovative methods and interventions that promote equity in health and healthcare delivery, particularly for marginalized groups. The event will revolve around the theme “Mitigating Bias and Advancing Health Equity in Clinical Algorithms in Healthcare.”

Algorithmic bias carries significant real-world implications that pervade various domains, including employment, housing, and healthcare. While many emerging methods are being employed to comprehend and mitigate algorithmic bias, critical gaps persist in the development and implementation of such vital approaches to advance health equity research and practice solutions.

Duke AI Health has prioritized algorithmic bias in health as a central focus of its mission to foster ethical and equitable data science. “I am thrilled to support Dr. Cary’s leadership in this essential domain and eagerly anticipate the expertise this event will bring together,” remarked Dr. Michael Pencina, Vice Dean for Data Science and Director of AI Health.

The Duke School of Nursing is deeply committed to mitigating the adverse social determinants of health and eradicating health inequities. ” I commend the efforts of Dr. Cary and the team at Duke AI Health in organizing the first Duke Symposium on Algorithmic Equity and Fairness in Health,” said Dr. Vincent Guilamo-Ramos, Dean and Bessie Baker Distinguished Professor in the Duke University School of Nursing. “This symposium will provide a valuable platform for experts to come together, share knowledge, and develop innovative solutions to advance health equity research and practice.” He went on to say, “I particularly encourage nurses to actively engage in these discussions and contribute to the ongoing efforts to create fair and unbiased algorithms in clinical settings and throughout the community where healthcare is delivered. Together, we can make a meaningful difference in promoting equity and fairness in healthcare.”

Save the date! The symposium is scheduled to take place in person at the Duke School of Nursing from March 13-14, 2024. Additional details and registration information will be announced in fall 2023. To stay informed about the event, we encourage individuals to sign up for the Duke AI Health mailing list and the Duke School of Nursing mailing list.

Media Contact:
Sarah Riddle; Manager, External Communications, Duke University School of Nursing
Phone: (919) 613-9778
Email: sarah.j.riddle@duke.edu

About Duke University School of Nursing
A diverse community of scholars and clinicians, Duke University School of Nursing is advancing health equity and social justice by preparing nurse leaders and innovators with a commitment to improving health outcomes through transformative excellence in education, clinical practice, and nursing science. Ranked as one of the leading nursing schools in the country, Duke School of Nursing focuses on improving the health of communities locally and globally by educating the nursing leaders of tomorrow and taking tangible steps to end health inequity, like the creation of www.DUSONtrailblazer.com, a set of conceptual and applied web resources for harmful social determinants of health mitigation.

About Duke AI Health
Duke AI Health is a pioneering initiative at Duke University that focuses on the ethical and equitable application of data science in healthcare. The mission of Duke AI Health is to drive innovation, research, and collaboration to advance health equity, improve patient outcomes, and transform healthcare delivery.

 

The Duke AI Health Data Science Fellowship Program is a 2-year training program in data science with direct application for healthcare. Designed for early-career data scientists with strong backgrounds in quantitative disciplines, the program is part of a multidisciplinary, campus-spanning initiative that applies machine learning and quantitative sciences to rich sources of healthcare and administrative data, using the insights gained to improve healthcare delivery, quality of care, and the health of individuals and communities.

Under the leadership of program director Lisa Wruck, PhD, and associate director Silvana Lawvere, PhD, the Data Science Fellowship program enrolled the first fellows in February 2020, and 12 fellows have participated in the program to date.

“Dr. Wruck and Dr. Lawvere have been integral to the success of this program,” said Michael Pencina, PhD, vice dean for data science and director of Duke AI Health. “Their expertise and commitment to the trainees has created a rigorous and supportive environment for them to learn and thrive, and I’m grateful to them for creating the success of the program.”

As Dr. Wruck and Dr. Lawvere step away from the program, the leadership will transition to Matt Engelhard, PhD as the faculty director and Andrew Olson, MPP as the senior operations leader. Dr. Engelhard is an Assistant Professor of Biostatistics and Bioinformatics and Mr. Olson is AI Health’s Associate Director, Policy Strategy and Solutions for Health Data Science.

A new video highlights the experience of the Duke Health Data Science Poster Showcase, held April 24, 2023 at the Mary Duke Biddle Trent Semans Center for Medical Education.

Created by Duke videographer Michael Blair with support from the Duke Center for Computational Thinking, the video features interviews by Matt Engelhard, PhD, an Assistant Professor of Biostatistics and Bioinformatics and faculty director of the AI Health Data Science Fellowship Program; and Hanxue Gu, a PhD student in the Electrical and Computer Engineering department and a member of the Mazurowski Lab who won the award for Best Computational Thinking Poster.

 

By Tiffany Torres

The Spring 2023 semester was comprised of 15 AI Health seminars that attracted 866 attendances, including people who attended multiple sessions.

 The AI Health seminar series builds upon the success of our previous “Plus Data Science” learning experiences from 2017 – 2021 under the leadership of Larry Carin, which convened 59 in-person learning experiences and 66 virtual learning experiences.

Since its launch in 2018, +DS has held a cumulative 140 learning experience sessions (both in-person and virtual).

The Spring 2023 semester featured 32 data experts from across multiple areas of interest.

Duke Departments and Schools Represented:

• Biostatistics and Bioinformatics
• Duke Health Technology Solutions
• Biomedical Imaging
• Family Medicine and Community Health
• School of Nursing
• Civil and Environmental Engineering
• Electrical and Computer Engineering
• Computer Science
• CTSI
• Radiology
• Pediatrics
• Neurosurgery
• Biomedical Engineering
• Population Health Sciences
• Geriatrics
• Office of Evaluation and Applied Research Partnership

We are looking forward to the Fall 2023 learning experiences, which will begin in September. A list of upcoming events can be found at https://aihealth.duke.edu/events/

An important study led by Duke’s David Ming, MD, and AI Health’s Benjamin Goldstein, PhD, and Nicoleta Economou, PhD, on the use of predictive modeling to identify children with complex health needs who are at high risk for hospitalization, was recently published in Hospital Pediatrics, the official journal of the American, Academy of Pediatrics. The study analyzed data from electronic health records and found that certain demographic, clinical, and health service use factors were associated with a higher risk of future hospitalization. The authors, including Duke’s Richard Chung, MD, and Ursula Rogers, BS, suggest that the use of predictive modeling can help identify children with complex health needs who may benefit from targeted interventions to prevent hospitalizations and improve outcomes. The study is accompanied by a commentary by University of Wisconsin Neil Munjal, MD, MS, titled ‘Machine Learning: Predicting Future Clinical Deterioration in Hospitalized Pediatric Patients,’ which describes the Duke researchers’ machine learning approach as “thought-provoking.”

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A poster showcase held on Monday, April 24, 2023 at the Mary Duke Biddle Trent Semans Center for Medical Education featured 28 posters in health data science. This cross-disciplinary event was hosted by multiple organizations, including Duke AI Health, the Laboratory for Transformative Administration, and the Center for Computational Thinking. Poster topics were centered around health data science and covered a wide range of topics including statistics, informatics, machine learning, data engineering, implementation, process engineering, technology development, and applications. The posters were submitted by people at all stages of their careers, including students, trainees, staff, and faculty. Information tables also shared programs and resources relevant to health data science at Duke.

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The Health Data Science poster showcase will be held in person on Monday, April 24 from 12:00-2:00 PM. We’re excited that this cross-disciplinary event will be hosted by multiple organizations, including Duke AI Health, the Laboratory for Transformative Administration, and the Center for Computational Thinking.

The poster display will take place in the Mary Duke Biddle Trent Semans Center for Medical Education (Trent Semans) on the 6th floor and we’ll serve light refreshments.

More than 25 posters will be presented, including Duke participants from: AI Health Fellowship Program; Biomedical Engineering; Clinical and Translational Science Institute (CTSI); Computer Science; Department of Biostatistics and Bioinformatics; Department of Internal Medicine; Department of Neurosurgery; Department of Surgery; Division of Geriatrics, Department of Medicine; Division of Hematology, Department of Medicine; Duke Clinical Research Institute (DCRI); Electrical and Computer Engineering (ECE); Duke Health Technology Solutions (DHTS); Laboratory for Transformative Administrative (LTA); Master of Management in Clinical Informatics (MMCi); OB-GYN; and Trinity College of Arts & Sciences.

Information tables will include programs from across Duke: The + Programs for Students; Duke AI Health; Biostatistics, Epidemiology, and Research Design (BERD); Center for Computational Thinking; Duke Data Analytics Community; and the Master of Management in Clinical Informatics.

Poster awards will include Best Computational Thinking Poster and the Good DEEDS Award (Ethical and Equitable Data Science).

Please join us! All are welcome, and light refreshments will be served.

The Coalition for Health AI (CHAI) released its highly anticipated “Blueprint for Trustworthy AI Implementation Guidance and Assurance for Healthcare” (Blueprint). The Blueprint addresses the quickly evolving landscape of health AI tools by outlining specific recommendations to increase trustworthiness within the healthcare community, ensure high-quality care, and meet healthcare needs. The 24-page guide reflects a unified effort among subject matter experts from leading academic medical centers and the healthcare, technology, and other industry sectors, who collaborated under the observation of several federal agencies over the past year.

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The Health Data Science poster showcase will be held on Monday, April 24 from 12:00-2:00 PM in-person in the Mary Duke Biddle Trent Semans Center for Medical Education (Trent Semans). We’re excited that this cross-disciplinary event will be hosted by multiple organizations, including Duke AI Health, the Laboratory for Transformative Administration, and the Center for Computational Thinking.

We invite any member of the Duke community to propose a poster entry for participation in this event, including students, trainees, staff, and faculty. This experience is intended to be especially valuable to individuals seeking to gain experience in presenting their work in front of a scientific audience, and the poster itself can become a valuable part of an academic portfolio.

Submit your poster topic at: https://duke.qualtrics.com/jfe/form/SV_1HuvnGKOY4YMa9w

The preferred deadline for poster topics to be submitted is Monday, March 13, 2023 by 11:59 PM (Eastern time).

Update on March 10: We’ve heard from several people that their research is ongoing, and we’ve decided to accept poster topics on an rolling basis, to allow everyone the full opportunity to participate.

Poster topics must be centered around health data science, but can cover a wide range of potential topics, such as statistics, informatics, machine learning, data engineering, implementation, process engineering, technology development, or applications. We especially encourage submissions describing experiences with Duke data sources. Student posters describing class projects (at both the undergraduate and graduate levels) are also encouraged.

After you submit your topic, you’ll then receive a poster template with the correct dimensions.

You’ll need to submit your finalized poster by Friday, April 14 in order to have it printed. If your poster is accepted, the event organizers will print it for you and you will have no cost to participate. The showcase will include poster judging, with recognitions including best poster.

We’re excited to do another poster session after the very successful December event, and we invite you to join us! Please email aihealth@duke.edu if you have any questions.

The fall 2022 semester was comprised of 9 AI Health seminars that attracted 463 attendances, including people who attended multiple sessions. Across all of 2022, the AI Health seminar series has hosted 22 virtual seminars with 1,820 cumulative attendances. The AI Health seminar series builds upon the success of our previous “Plus Data Science” learning experiences from 2017 – 2021 under the leadership of Larry Carin, which convened 59 in-person learning experiences and 66 virtual learning experiences. Since its launch in 2018, +DS has held a cumulative 125 learning experience sessions (both in-person and virtual). – Metrics by Tiffany Torres

Current medical standards for accessing stroke risk perform worse for Black Americans than they do for white Americans, potentially creating a self-perpetuating driver of health inequities. A study, led by Duke Health researchers and appearing online Jan. 24 in the Journal of the American Medical Association, evaluated various existing algorithms and two methods of artificial intelligence assessment that are aimed at predicting a person’s risk of stroke within the next 10 years. The study found that all algorithms were worse at stratifying the risk for people who are Black than people who are white, regardless of the person’s gender. The implications are at the individual and population levels: people at high risk of stroke might not receive treatment, and those at low or no risk are unnecessarily treated.

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Last fall AI Health held an in-person workshop designed to give hands-on experience in working with medical digital pathology images using machine learning. See highlights from the afternoon in a video created by our partners in the Center for Computational Thinking. The concept of “do machine learning in just one afternoon!” was very successful, and we appreciate the participation from all those who attended. We are currently working to design more such studios, and please join our mailing list if you’d like to be notified for upcoming events.

WATCH HERE

Save the date: February 14, 2023, 12:00 PM EST: Duke AI Health’s Nicoleta Economou, PhD, joins Duke DHTS’s Armando D. Bedoya MD MMCi, to present: ‘Algorithm-Based Clinical Decision Support (ABCDS) Oversight: A framework for the governance and evaluation of algorithms to be deployed at Duke Health.’ During the webinar, which is open to members internal and external to Duke, Drs. Economou and Bedoya will discuss highlights from their recent paper published in the Journal of the American Medical Informatics Association (JAMIA).

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Recent years have seen growing interest in the use of artificial intelligence tools for healthcare applications, including diagnosis, risk prediction, clinical decision support, and resource management. Capable of finding hidden patterns within the enormous amounts of data that reside in patient electronic health records (EHRs) and administrative databases, these algorithmic tools are diffusing across the world of patient care. Often, health AI applications are accompanied by assurances of their potential for making medical practice better, safer, and fairer. The reality, however, has turned out to be more complex.

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Duke AI Health’s HDS research and education hub held a successful Poster Showcase on December 6, 2022, featuring the work of 16 students and fellows. Hosted by Ricardo Henao, PhD, and Shelley Rusincovitch, MMCi, the presenters included members of the HDS fall 2022 student cohort, fellows in the AI Health Data Science Fellowship program, as well as members of AI Health’s Spark Imaging Initiative and Duke Biostatistics & Bioinformatics’s BCTIP program.

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Congratulations to AI Health Faculty Council member Ben Goldstein, PhD, and Duke Children’s Health & Discovery Initiative Director Jillian Hurst, PhD, for their success in leading the Electronic Health Record (EHR) Study Design Workshop from December 5-9, 2022. The course was offered as a virtual 5-day class providing foundational lectures and hands-on studios on the fundamentals of working with, and designing EHR-based studies. The inaugural workshop generated a great deal of enthusiasm and every seat in the course was filled within 6 weeks of course announcement.

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Join Duke AI Health Director Michael Pencina, PhD, as he takes part in discussions with expert panelists convened from government, industry and academia to discuss recent advances in health AI, including structural biological modeling, computer vision algorithms, and ethical frameworks for employing AI in healthcare. This virtual event, “Modeling Equitable AI in Digital Health,” is hosted by MITRE and will take place starting at 4:00 PM EST on Thursday, December 8, 2022.

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DCRI Science and Digital Officer Eric Perakslis, PhD, shares a deeply personal perspective on a recent federal mandate that expands patients’ access to data stored in their EHRs – but also carries its own potential for risks. In his essay, Dr. Perakslis combines the patient and tech expert viewpoints as he surveys the “lumpy, bumpy, imperfect progress” toward better data transparency while undergoing cancer diagnosis and treatment.

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Duke AI Health is pleased to announce the Duke Electronic Health Records Study Design Workshop (EHR-SDW) 2022. The workshop will be offered in December as a virtual five-day class that provides foundational lectures and hands-on studios on the fundamentals of working with and designing EHR based studies. The EHR-SDW is targeted toward individuals interested in learning about how to work with and conduct studies using electronic health records (EHR) data. EHR data are a widely available form of real-world data that have become standard in studies ranging from clinical trials, comparative effectiveness, risk prediction, and population health. This workshop is offered through Duke AI Health’s Health Data Science (HDS) program and builds on the success of our highly successful Machine Learning Schools, with 11 events held since 2017.

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The AI Health Data Science Fellowship Program is a two-year training program focused on data science with healthcare applications, designed for early-career data scientists with strong backgrounds in quantitative disciplines. Launched in fall of 2019, the program currently has 5 fellows, 2 staff data scientists, and 5 alumni. The program recently came together in-person for lunch for the first time since the pandemic. They gathered to welcome 2 new members: new fellow Angel Huang and new Data Scientist, John Rollman.

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This in-person workshop presented by Ricardo Henao, PhD; Associate Professor, Department of Biostatistics and Bioinformatics; Chief AI Scientist, Duke AI Health, Akhil Ambekar, MS; Fellow, AI Health Data Science Fellowship Program, with Shelley Rusincovitch, MMCi; Managing Director, Duke AI Health, will give you hands-on experience in working with medical digital pathology images using machine learning. Our use case will be in whole slide images of lymph node sections. We will use the CAMELYON16 dataset (https://camelyon16.grand-challenge.org/), which consists of 400 hematoxylin and eosin-stained whole-slide images. During the workshop, you will learn how to retrieve, manage, and process these images, then apply a machine learning model based on a neural network architecture to classify image regions as normal or malignant. The techniques you learn will also be broadly applicable to other types of medical imaging.

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Duke AI Health congratulates Chief AI Health Scientist Ricardo Henao, PhD, on his promotion to the rank of Associate Professor in the Department of Biostatistics and Bioinformatics in the Duke University School of Medicine. Dr. Henao is a major presence in health data science at Duke, where his leadership and expertise in machine learning methods and implementation have made him a sought-after collaborator and instructor. “Dr. Henao is a major asset to Duke AI Health and to the larger Duke community,” said Michael Pencina, PhD, director of Duke AI Health and vice dean for data science at the School of Medicine. “We feel fortunate to be able to benefit from such a rare combination of talent and knowledge spanning research, application, and teaching.”

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Duke AI Health Director and Vice Dean for Data Science Michael J. Pencina, PhD, has achieved a major academic milestone: according to Google Scholar’s analytics, he has recently passed the 100,000 mark for academic citations of his work. Pencina, who in addition to his leadership role in Duke’s efforts to develop, evaluate, and implement ethical and equitable data science, has also worked extensively on the development and evaluation of risk prediction models and clinical trial designs.

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As a member of the Coalition for Health AI, Duke AI Health is working to develop a consensus-driven framework to drive high-quality health care through the adoption of credible, fair, and transparent health AI systems. The coalition is convening a series of virtual workgroup sessions to define core principles and has published a white paper from its first meeting: “Bias, Equity, and Fairness.” Please review the paper and submit your feedback by Sept. 15: https://bit.ly/3wbAXQx. With the help of your ideas, the Coalition for Health AI can advance towards establishing clear and appropriate guidelines and guardrails for the fair, ethical, and useful application of AI and machine learning in health care settings.

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New research led by Duke AI Health Director Michael Pencina, PhD, published recently in the journal Circulation, looked at the value of using a genomic test to predict the future risk of heart disease. Pencina and colleagues found that the genomic test, referred to as the polygenic risk score (PRS), only marginally added to the predictive information obtained through the assessment of traditional risk factors, concluding that the PRS “had minimal clinical utility”.

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We invite Duke students to apply for the Health Data Science (HDS) fall research program. This competitive program, based in Duke AI Health, is designed to allow students who have previous experience in data science to continue their engagement with substantive applied projects. The HDS Research Program offers Duke students, both undergraduate and graduate, the opportunity to be a part of research teams applying advanced machine learning (deep learning) to important areas of medicine. Participating students will be mentored by leading Duke faculty involved in data science research, often with guidance by practicing clinicians. The fall will culminate in a showcase session where student teams will present their results.

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Ben Goldstein and Jillian Hurst share their experiences developing the Clinical Research with Electronic Health Records (CR-EHR) to bring together investigators with different backgrounds to learn how to collaborate on the design and execution of EHR-based studies.

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